The Western Pacific Coasts (WPC) are the outlets of many large Asian rivers. In recent years, the interplay of climate changes and human activities has persistently altered the suspended sediment concentrations (SSC) in the WPC, triggering substantial shifts in coastal ecosystems. However, the scarcity of coastal observation stations hampered comprehensive investigations at large scales. This study employed three representative SSC retrieval models and utilized Landsat images acquired from 1990 to 2020 to estimate the SSC in the WPC with a focused endeavor to dissect the intricate spatial and temporal variability of SSC in the region. The findings revealed the following insights: (1) The outcomes derived from the three distinct SSC models consistently manifested a discernible decreasing pattern in SSC changes over the past three decades across all six major estuaries (Liao River Estuary, Yellow River Estuary, Yangtze River Estuary, Hangzhou Bay, Pearl River Estuary, and Mekong River Estuary). (2) The seasonal attributes of the six major estuaries differed, primarily due to distinct dominant influencing factors like precipitation, upstream sediment load, wind, and tides. (3) Collectively, SSC tends to be relatively higher in the Yangtze River Estuary, Hangzhou Bay, and Yellow River Estuary, while the Pearl River and Mekong River Estuaries exhibit relatively lower levels. Notably, the SSC exhibited distinct spatial traits along the coastlines of different estuaries. (4) SSC in the non-estuarine regions along the WPC, a similar significant declining trend in SSC is observed as in the estuaries, albeit the rate of decline generally appeared to be less pronounced. Furthermore, regions with faster rates of SSC reduction are typically concentrated near major estuaries in the northern part of the Coasts. The decline in estuarine SSC plays an important role in the overall decrease in SSC across the WPC. These study outcomes held substantial significance for advancing the stability and sustainable evolution of the WPC.

According to online monitoring data on atmospheric ozone and the pollution characteristics of its precursors obtained in Jinan in June 2021, we analyzed different sites: urban sites (city monitoring station, Quancheng Square), an industrial park site (oil refinery), and a suburban site (Paomaling). The relative incremental reactivity of different precursors was calculated using a photochemical observation-based model to explore the sensitivity of O₃ generation at each site and to draw a curve using the empirical kinetics modeling approach. The PMF model was used to analyze the origin of volatile organic compounds (VOCs) pollution in Jinan. The results showed that the concentration of O₃ at the industrial park was higher than that in the urban area in Jinan, which may be related to the fact that ozone precursor concentrations in the industrial park were significantly higher than those in the urban area (the AVOCs concentration at the industrial park site was 56.9 ppbv, approximately twice that of the urban site), and there are emission peaks at night; alkanes, oxygenated compounds, and halogenated hydrocarbons were the main components of the AVOCs, and olefins, alkanes, and aromatic hydrocarbons were the main active components in Jinan. The O₃ generation in urban areas generally occurred in the VOCs-sensitive zones, while the O₃ generation in the other areas occurred in the VOCs-NO_x transition zone; there was a clear diurnal variation in the sensitivity of the industrial park, with the site being in the obvious VOCs-sensitive zone from nighttime to morning hours and shifting to the VOCs-NO_x transition zone in the afternoon hours; the relative incremental reactivity (RIR) value of AVOCs for O₃ generation in Jinan was the largest, and olefins were the most sensitive component of O₃. The AVOCs in Jinan mainly originated from motor vehicle exhaust, oil and gas volatilization, industrial emissions, and solvent use, and ozone prevention and control in summer should strengthen the control of these sources.

Vehicle emissions have become a significant contributor to urban air pollution. However, studies specific to city-level vehicle emission inventories are still scarce and tend to be outdated. This study introduces a methodology for developing high-resolution monthly vehicle emission inventories. We applied this methodology to Changzhou in 2022 to analyze emission characteristics and generate gridded emission data with a resolution of 0.01° × 0.01°. The results show that the total vehicle emissions of carbon monoxide (CO), volatile organic compounds (VOCs), nitrogen oxides (NO_x), and fine particulate matters (PM_2.5) in Changzhou are 39.69, 8.68, 18.6, and 0.56 Gg, respectively. Light-duty passenger vehicles are the main contributors to CO (74.3%) and VOCs (86.1%) emissions, while heavy-duty trucks play a significant role in NO_x (50.7%) and PM_2.5 (34.7%) emissions. Gasoline vehicles are mainly responsible for CO (78.6%) and VOCs (91.4%) emissions, while diesel vehicles are the primary source of NO_x (81.1%) and PM_2.5 (70.6%) emissions. Notably, China IV vehicles have the highest emission contribution rates (ranging from 32.5% to 44.9%). Seasonally, emissions peak in winter and are lowest in April. Spatially, emission intensity is higher in the northeast of Changzhou compared to the west and south. The methodology presented in this study offers a valuable tool for developing comprehensive city-level emission inventories, and the results provide critical insights that can inform the formulation of effective environmental policies.

China has identified the synergistic reduction of pollution and carbon emissions as a crit ical component of its environmental protection and climate mitigation efforts. An assessment of this synergy can provide clarity on the strategic management of both air pollution and carbon emissions. Due to the extensive regional differences in China, the spatial effects of influencing factors on this synergy exhibit variation across different provinces. In this study, the reduction indexes of PM_2.5 and CO₂ were calculated based on their reduction bases, reduction efforts, and reduction stabilities across provinces. Then, the synergistic reduction effect was assessed using an exponential function with the PM_2.5 reduction index as the base and the CO₂ reduction index as the exponent. Next, the MGWR model was applied in order to analyze the influencing factors of the synergistic reduction effect, considering natural settings, socioeconomic conditions, and external emission impacts. Finally, the k-means clustering method was utilized to classify provinces into different categories based on the degree of impact of each influencing factor. The results indicated that air circulation, vegetation, tertiary industry ratio, and emission reduction efficiency are major impact indicators that have a positive effect. The topography and emissions from neighboring provinces have a statistically significant negative impact. The spatial influences of different factors exhibit a distribution trend characterized by a high-high cluster and a low-low cluster. A total of 31 provinces are divided into three categories, and suggestions on the corresponding category are proposed, to provide a scientific reference to the synergistic reduction of PM_2.5 and CO₂.